The data science design manual:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cham, Switzerland
Springer
[2017]
|
| Schriftenreihe: | Texts in computer science
|
| Schlagworte: | |
| Online-Zugang: | Inhaltstext Inhaltsverzeichnis |
| Beschreibung: | xvii, 445 Seiten Illustrationen, Diagramme (farbig) 25. cm |
| ISBN: | 9783319856636 9783319554433 |
Internformat
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Steven S Skiena
The Data Science Design Manual
a Springer
Contents
1 What is Data Science?
1 1 Computer Science, Data Science, and Real Science
1 2 Asking Interesting Questions from Data - - -4:5
121 The Baseball Encyclopedia 2222200000
122 The Internet Movie Database (IMDb)
123 Google Ngrams 2:2 eet
124 New York Taxi Records 202e ee eee
1 3 Properties of Data 2 sure nenne
131 Structured vs Unstructured Data 2220000
132 Quantitative vs Categorical Data 0
133 Big Data vs Little Data 6 ee ee eee
1 4 Classification and Regression 2 0020p eee ees
1 5 Data Science Television: The Quant Shop ---
15 1 Kaggle Challenges : - ee ee ee ee
1 6 About the War Stories 2220er een
1 7 War Story: Answering the Right Question -5-
1 8 Chapter Notes 202 ee eee
1 9 Exercises 2 ee
2 Mathematical Preliminaries
2 1 Probability 2 ee ee
211 Probability vs Statistics 6 ee ee
212 Compound Events and Independence -- -
213 Conditional Probability © 50440
214 Probability Distributions © 0 6 -- 6 ee ee
2 2 Descriptive Statistics 6 es
221 Centrality Measures eee ee ee es
222 Variability Measures 6 ee ee
223 Interpreting Variance 6 eee ee ee ees
224 Characterizing Distributions +25065
2 3 Correlation Analysis 2 00 0 ee
231 Correlation Coefficients: Pearson and Spearman Rank
232 The Power and Significance of Correlation
233 Correlation Does Not Imply Causation!
xi
xü
CONTENTS
234 Detecting Periodieities by Autocorrelation ++ 46
2 4 Logarithms 2 2 ee 47
241 Logarithms and Multiplying Probabilities ---- 48
242 Logarithms and Ratios -- ++ eee rete 48
243 Logarithms and Normalizing Skewed Distributions 49
2 5 War Story: Fitting Designer Genes 26-2 ee ee et 50
2 6 Chapter Notes © 6 eee ee ts 52
9 7 Exercises 0 ee ee ee eee 53
Data Munging 57
3 1 Languages for Data Science - 26 - eee ett 57
311 The Importance of Notebook Environments ---- 59
312 Standard Data Formats 2 ee ee ett 61
3 2 Collecting Data 6 et 64
321 Hunting - ee ee nn 64
322 Scraping 2 et 67
323 Logging - 1 eee et 68
3 3 Cleaning Data 22 2 eee eee 69
331 Errors vs Artifacts 2220 ee eee ee et 69
332 Data Compatibility - - eee eee et 72
333 Dealing with Missing Values - 2+ e ce 76
33 4 Outlier Detection 6 2 26 eee ee es 78
3 4 War Story: Beating the Market - 6-2 ee tres 79
3 5 Crowdsoureing --- ee te 80
351 The Penny Demo --- +++ ee eer ere 81
352 When isthe Crowd Wise? 6 0 eee ee rts 82
353 Mechanisms for Aggregation --- ++ see 83
354 Crowdsoureing Services +--+ eee er tre 84
355 Gamification 2 0 02 ee ee ee eet 88
3 6 Chapter Notes 2 2-0 eee eee es 90
3 7 Exercises -- eee te en nn 90
Scores and Rankings 95
4 1 The Body Mass Index (BMI) -- +--+ eee 96
4,2 Developing Scoring Systems - -- +--+ eee eer rte 99
421 Gold Standards and Proxies -- eee eee 99
422 Scores vs Rankings - +--+ eee eer ere 100
423 Recognizing Good Scoring Functions ---+-+-: 101
43 Z-scores and Normalization ----- ese eee rere 103
4 4 Advanced Ranking Techniques - - +--+ eee eters 104
441 Elo Rankings ---- 0 ee eeeeeeete 104
442 Merging Rankings ------ 2 eer ere ere 108
443 Digraph-based Rankings -- +--+ ++ rte 109
444 PageRank ---- +e eee ee etre 111
4 5 War Story: Clyde’s Revenge --- +--+ eer rere 111
4 6 Arrow’s Impossibility Theorem ------ rennen 114
CONTENTS xiii
4 7 War Story: Who’s Bigger? ee ee 115
4 8 Chapter Notes 26 ee ee ee 118
4 9 Exercises 2 eee 119
5 Statistical Analysis 121
5 1 Statistical Distributions 6 ee ee ee ee 122
511 The Binomial Distribution - - Herner 123
512 The Normal Distribution rec 2s eae 124
513 Implications of the Normal Distribution 126
514 Poisson Distribution - - 50 ee ee eee 127
515 Power Law Distributions - 6---- 0046 129
5 2 Sampling from Distributions -- 6 2222 5005- 132
521 Random Sampling beyond One Dimension 133
5 3 Statistical Significance 6 ee ee es 135
531 The Significance of Significance --- +54: 135
532 The T-test: Comparing Population Means 137
533 The Kolmogorov-Smirnov Test -- ----++5- 139
534 The Bonferroni Correction 1 2 000 eee eee 141
535 False Discovery Rate ee ee ee ees 142
5 4 War Story: Discovering the Fountain of Youth? - 143
5 5 Permutation Tests and P-values 2 022 eee eee 145
551 Generating Random Permutations - -- 147
552 DiMaggio’s Hitting Streak - 2-6 ee ee ees 148
5 6 Bayesian Reasoning 2 0+ eee eee eee 150
5 7 Chapter Notes 2 - cee ee ee ee eee 151
5 8 Exercises 0 ee 151
6 Visualizing Data 155
6 1 Exploratory Data Analysis 6 6 ete ee es 156
611 Confronting a New Data Set 2 6 ee ee ees 156
612 Summary Statistics and Anscombe’s Quartet 159
613 Visualization Tools 2 2 es 160
6 2 Developing a Visualization Aesthetic 6 602 eee 162
621 Maximizing Data-Ink Ratio 6 ee ee ee eee 163
622 Minimizing the Lie Factor 2-26 + ee ee eee 164
623 Minimizing Chartjunk 2 2 ee ee ee ee 165
624 Proper Scaling and Labeling - -- +--+ eee 167
625 Effective Use of Color and Shading - --5 168
626 The Power of Repetition 262 eee eee ee 169
6 3 Chart Types 2 0 ee eee ee ee es ee 170
631 Tabular Data 1 ee ee es 170
632 Dot and Line Plots - 50+ e eee eee 174
633 Scatter Plots 2 ee ee ee ee ee es 177
634 Bar Plots and Pie Charts 2 6 ee eee eee 179
635 Histograms 0 6 ee ee ee ee es 183
636 DataMaps 2 eee eee ee ee 187
xiv CONTENTS
6 4 Great Visualizations 220 es 189
641 Marey’s Train Schedule - 02 +22 e+ 189
642 Snow’s Cholera Map 0 0 ee ee eee 191
643 New York’s Weather Year 0 0 ee es 192
6 5 Reading Graphs 1 6 ee es 192
651 The Obseured Distribution 2 2 193
652 Overinterpreting Variance 2 een 193
6 6 Interactive Visualization 1 0 eee ee 195
6 7 War Story: TextMapping the World 6 220+ ere 196
6 8 Chapter Notes 2 0c ee ee es 198
6 9 Exercises 2 ee ee en 199
Mathematical Models 201
7 1 Philosophies of Modeling 2 cr rennen 201
71 1 Occam’sRazor - 2er nenenen 201
71 2 Bias-Variance Trade-Ofls 2 2 ee 202
713 What Would Nate Silver Do? 2 2 eee ee ee 203
72A Taxonomy of Models - -0- 0-0 ee teres 205
721 Linear vs Non-Linear Models 2 ---- 2-2 206
722 Blackbox vs Descriptive Models ---- +--+ 0 206
723 First-Prineiple vs Data-Driven Models + - 207
724 Stochastic vs Deterministic Models ------- 208
725 Flat vs Hierarchical Models 0 00 209
7 3 Baseline Models -- 20er es 210
731 Baseline Models for Classification - -----: 210
732 Baseline Models for Value Prediction - --+--- 212
7 4 Evaluating Models 2202 20 20 eee ees 212
74 1 Evaluating Classifiers - - 0-05 eee eee 213
742 Receiver-Operator Characteristic (ROC) Curves -- 218
743 Evaluating Multielass Systems - He + 219
744 Evaluating Value Prediction Models - ----- 221
75 Evaluation Environments --05+ 00+ eee 224
751 Data Hygiene for Evaluation - cc 225
752 Amplifying Small Evaluation Sets ec 226
7 6 War Story: 100% Accuracy 2 2 2e rennen 228
7 7 Simulation Models 2 00-2 ee et 229
7 8 War Story: Calculated Bets 0 0 20 0-5 eee 230
7 9 Chapter Notes 2 6 ee ee es 233
7 10 Exercises 2 0 ee ee es 234
Linear Algebra 237
8 1 The Power of Linear Algebra 222 02 + ees 237
811 Interpreting Linear Algebraic Formulae -----: 238
812 Geometry and Vectors --- 220522 ree 240
8 2 Visualizing Matrix Operations 2 2 -- 5-2 eee ee 241
821 Matrix Addition
CONTENTS xv
822 Matrix Multiplication 2 2202er 243
823 Applications of Matrix Multiplication 244
824 Identity Matrices and Inversion 2 22 2200 248
825 Matrix Inversion and Linear Systems 250
826 Matrix Rank 2 2 2222 nun 251
8 3 Factoring Matrices © ee ee 252
831 Why Factor Feature Matrices? © 000 00005 252
832 LU Decomposition and Determinants - 254
8 4 Eigenvalues and Higenvectors 0 eee ees 255
841 Properties of Higenvalues 2 6 0-2 + ee eee 255
842 Computing Higenvalues 1 0 ee ee ee ee 256
8 5 Eigenvalue Decomposition 2 eee ee es 257
851 Singular Value Decomposition - - 0055 258
852 Principal Components Analysis -+50--- 260
86 War Story: The Human Factors - 40 - ee eee eee 262
8 7 Chapter Notes 2 2 ee ee es 263
8 8 Exercises 6 0 ee 263
9 Linear and Logistic Regression 267
9 1 Linear Regression 2 6 ee ern 268
911 Linear Regression and Duality 0-005 268
912 Error in Linear Regression 1 eee ee eee 269
91 3 Finding the Optimal Fit -2 054200 270
9 2 Better Regression Models 1 26 eee eee eee ee 272
921 Removing Outliers 2 0 ee ee 272
922 Fitting Non-Linear Functions - 50-5 273
923 Feature and Target Scaling - -- 50-200 274
924 Dealing with Highly-Correlated Features - 277
9 3 War Story: Taxi Deriver ee ee ees 277
9 4 Regression as Parameter Fitting © -- 0 +4 eee ees 279
941 Convex Parameter Spaces 00002 eee eee 280
942 Gradient Descent Search 2222er 281
943 What isthe Right Leaming Rate? re 00 283
944 Stochastice Gradient Descent 2 2 00 285
9 5 Simplifying Models through Regularization vr er + 286
951 Ridge Regression ron nennen 286
952 LASSO Regression 6 ee eee ee ee es 287
953 Trade-Offs between Fit and Complexity - 288
9 6 Classification and Logistic Regression 1 sete eee 289
961 Regression for Classification ---- 2 eee eee 290
962 Decision Boundaries 2 ee ee ee es 291
963 Logistic Regression 6 eee ee es 292
9 7 Issues in Logistie Classification 2 eee eee eee 295
971 Balanced Training Classes creme 295
972 Multi-Class Classification 2222 297
973 Hierarchical Classification 6 ee ee ee ee 298
xvi CONTENTS
974 Partition Functions and Multinomial Regression - - 299
9 8 Chapter Notes 2 00 ee et es 300
9 9 Exercises 2 ee ee ees 301
10 Distance and Network Methods 303
10 1 Measuring Distances 6-6 eee ee es 303
10 1 1 Distance Metrics : 6 ee ee ee ee eee 304
10 1 2 The Lk Distance Metrie ee es 305
10 1 3 Working in Higher Dimensions rer cr 307
10 1 4 Dimensional Pgalitarianism 2-+- ++ eee 308
10 1 5 Points vs Vectols ee ee ee 309
10 1 6 Distances between Probability Distributions - 310
10 2 Nearest Neighbor Classification - - -- +e ee eee 311
10 2 1 Seeking Good Analogies re rerereen 312
10 2 2 k-Nearest Neighbors 2 6 02+ eee eee 313
10 2 3 Finding Nearest Neighbors rer ren 315
10 2 4 Locality Sensitive Hashing - --- +--+ e eee re 317
10 3 Graphs, Networks, and Distances ------ +e etre 319
10 3 1 Weighted Graphs and Induced Networks - --- 320
10 3 2 Talking About Graphs ernennen 321
10 3 3 Graph Theory -- 2-2-2 eee eee ees 323
10 4 PageRank 2 ee ee 325
10 5 Clustering - - ---- ee et 327
10 5 1 k-means Clustering -- ee rennen 330
10 5 2 Agglomerative Clustering - - ce rereee 336
10 5 3 Comparing Clusterings - - -- rer ee rennen 341
10 5 4 Similarity Graphs and Cut-Based Clustering - -- 341
10 6 War Story: Cluster Bombing 2 - 22 eee 344
10 7 Chapter Notes 2000 eee ee es 345
10 8 Exercises 2 ee ee ne 346
11 Machine Learning 351
11 1 Naive Bayes ee et 354
11 11 Formulation 2 ee ee ee et 354
11 1 2 Dealing with Zero Counts (Discounting) ---- 356
11 2 Decision Tree Classifiers - -- 2er eer rennen 357
11 2 1 Constructing Decision Trees 2-6-0 ee eee 359
11 2 2 Realizing Exclusive Or - ---sereeeeen 361
11 2 3 Ensembles of Decision Trees - -- rer 362
11 3 Boosting and Ensemble Learning -------- +++ -eere 363
11 3 1 Voting with Classifiers - --- eee eee eee 363
11 3 2 Boosting Algorithms ---- +--+ eee eee 364
11 4 Support Vector Machines 2 2-2-2 teeters 366
11 4 1 Linear SVMs 2-2-2 ee ee ee tt 369
11 4 2 Non-linear SVMs 0-6-2 ee ee ee ee ee 369
11 43 Kermels - 0 000 eee eee te eee 371
CONTENTS
11 5 Degrees of Supervision
11 5 1 Supervised Learning
11 5 2 Unsupervised Learning
11 5 3 Semi-supervised Learning
11 5 4 Feature Engineering
11 6 Deep Learning -
11 6 1 Networks and Depth
11 6 2 Backpropagation
11 6 3 Word and Graph Embeddings --+-46%
11 7 War Story: The Name Game
11 8 Chapter Notes 2
11 9 Exercises ---
12 Big Data: Achieving Scale
12 1 What is Big Data?
12 1 1 Big Data as Bad Data
12 1 2 The Three Vs
12 2 War Story: Infrastructure Matters
12 3 Algorithmics for Big Data
12 3 1 Big Oh Analysis
12 3 2 Hashing - -
12 3 3 Exploiting the Storage Hierarchy 05+
12 3 4 Streaming and Single-Pass
12 4 Filtering and Sampling -
Algorithms - - -
12 4 1 Deterministic Sampling Algorithms ---
12 4 2 Randomized and Stream Sampling -- --+--
12 5 Parallelism - - 4--
12 5 1 One, Two, Many
12 5 2 Data Parallelism
12 5 8 Grid Search
12 5 4 Cloud Computing Services
12 6 MapReduce vr
12 6 1 Map-Reduce Programming
12 6 2 MapReduce under the Hood - cr er
12 7 Societal and Ethical Implications
12 8 Chapter Notes 4 --
12 9 Exercises 2:20
13 Coda
13 1 Geta Job! - 2-
13 2 Go to Graduate School!
13 3 Professional Consulting Services
14 Bibliography |
| any_adam_object | 1 |
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| dewey-full | 004 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 004 - Computer science |
| dewey-raw | 004 |
| dewey-search | 004 |
| dewey-sort | 14 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Book |
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| owner | DE-91G DE-BY-TUM DE-11 DE-1050 DE-12 DE-863 DE-BY-FWS DE-83 DE-1043 |
| owner_facet | DE-91G DE-BY-TUM DE-11 DE-1050 DE-12 DE-863 DE-BY-FWS DE-83 DE-1043 |
| physical | xvii, 445 Seiten Illustrationen, Diagramme (farbig) 25. cm |
| publishDate | 2017 |
| publishDateSearch | 2017 |
| publishDateSort | 2017 |
| publisher | Springer |
| record_format | marc |
| series2 | Texts in computer science |
| spellingShingle | Skiena, Steven S. 1961- The data science design manual Data Science (DE-588)1140936166 gnd |
| subject_GND | (DE-588)1140936166 |
| title | The data science design manual |
| title_auth | The data science design manual |
| title_exact_search | The data science design manual |
| title_full | The data science design manual Steven S. Skiena |
| title_fullStr | The data science design manual Steven S. Skiena |
| title_full_unstemmed | The data science design manual Steven S. Skiena |
| title_short | The data science design manual |
| title_sort | the data science design manual |
| topic | Data Science (DE-588)1140936166 gnd |
| topic_facet | Data Science |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=0c5a2b4b6b1e4e0d92bba65a273530ae&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029756206&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT skienastevens thedatasciencedesignmanual |
Beschreibung
THWS Würzburg Zentralbibliothek Lesesaal
| Signatur: |
1000 ST 530 S628 |
|---|---|
| Exemplar 1 | ausleihbar Missing Vormerken |